Camera-based surveillance is widely employed to detect against crime such as burglaries and vandalism. Recently, the commoditization of video cameras and the advances in computer vision have lowered the barrier of deploying a small-scale camera-based surveillance system. These small-scale systems may be sufficient for recording activities in the immediate area, but some activities do not appear suspicious until data from multiple cameras is analyzed in aggregate. These types of activities include strangers knocking on several doors in a neighborhood (i.e., to figure out which houses are empty) and cars cruising the neighborhood without stopping anywhere (i.e., as part of reconnaissance prior to a burglary).
If users of the small-scale systems shared data with each other, or with a third party such as law enforcement or a monitoring company, many more suspicious activities could be detected than if each user merely viewed his or her own video cameras. However, residents and businesses may be reluctant to share data from their respective video cameras due to privacy concerns. Thus, the ability to detect suspicious activity by making inferences across data received from multiple cameras deployed by multiple different users is hindered by the respective users' reluctance to sacrifice privacy by sharing their video with others.